data_helpers {mapbayr}

Data helpers: functions to build the dataset

Description

Use adm_rows(), obs_rows() and add_covariates() to create or modify a dataset from scratch, from a pre-existing dataset, or from a dataset stored into a 'mrgsolve' model.

Details

Instead of importing a '.csv' file, or painfully build a data set with a call to data.frame() and mind how to format the data, you can pass information about:

• administrations with adm_rows(),

• observations with obs_rows(),

• covariates with add_covariates(),

all being called jointly with a pipe (⁠%>%⁠ or ⁠|>⁠). These functions can be used to create or modify a dataset as a proper data.frame, or to create or modify a dataset within a 'mrgsolve' model (⁠@args$data⁠ slot). The latter is particularly useful in order to:

• automatically use default administration and observation compartments,

• automatically duplicate rows if there are several depot compartments,

• automatically set rate = -2 if model has zero-order absorption pathways,

• automatically duplicate rows if concentrations of Parent drug and Metabolite are observed together,

• automatically add "Amount Of Last Administration" and "Time Of Last Administration" variables if these are covariates,

• subsequently call mrgsim() or mapbayest().

Examples

library(magrittr)
# First option: work with a data.frame

adm_rows(amt = 1000, cmt = 1, addl = 4, ii = 12) %>%
  obs_rows(time = c(12.3, 45.6), DV = c(.111, .222), cmt = 2) %>%
  obs_rows(time = 48, cmt = 2) %>%
  add_covariates(BW = 90, SEX = 0, TOLA = TRUE)

# You can even inform "time" using date and hours:
adm_rows(amt = 1000, cmt = 1, addl = 4, ii = 12, .datehour = "2022-01-01 11:11:11") %>%
  obs_rows(.datehour = "2022-01-02 22:22:22", DV = 0.111, cmt = 2)

# Second option: work with a dataset within a 'mrgsolve' model

mod <- exmodel(add_exdata = FALSE)
# call `mrgsolve::see(mod)` to see how default compartment were coded
adm_cmt(mod)
obs_cmt(mod)

mod %>%
  adm_rows(amt = 10000) %>%
  obs_rows(time = c(1.5, 4.4, 7.5, 24.6), DV = c(91.2904, 110.826, 79.384, 20.6671)) %>%
  # get_data() # for curiosity, you can extract the data set at this step
  mapbayest()